Chronic lung disease and multiple sclerosis: Incidence, prevalence, and temporal trends
Bibliographic record
Abstract
OBJECTIVES: We aimed to estimate the incidence and prevalence of chronic lung disease (CLD), including asthma and chronic obstructive pulmonary disease, in the MS population versus a matched cohort from the general population. METHODS: We used population-based administrative data from four Canadian provinces to identify 44,452 persons with MS and 220,849 age-, sex- and geographically-matched controls aged 20 years and older. We employed a validated case definition to estimate the incidence and prevalence of CLD over the period 1995-2005, and used Poisson regression to assess temporal trends. RESULTS: In 2005, the crude incidence of CLD per 100,000 persons was 806 (95%CI: 701-911) in the MS population, and 757 in the matched population (95%CI: 712-803). In 2005, the crude prevalence of CLD was 13.5% (95%CI: 13.1-14.0%) in the MS population, and 12.4% (95%CI: 12.3-12.6%) in the matched population. Among persons aged 20-44 years, the average annual incidence of CLD was higher in the MS population than in the matched population (RR 1.15; 95%CI: 1.02-1.30), but did not differ between populations for those aged ≥45 years. The incidence of CLD was stable, but the prevalence of CLD increased 60% over the study period. CONCLUSION: CLD is relatively common in the MS population. The incidence of CLD has been stable over time, but the prevalence of CLD has increased. Among persons aged 20-44 years, CLD is more common in the MS population than in a matched population. Given the prevalence of CLD in the MS population, further attention to the effects of CLD on outcomes in MS and approaches to mitigating those effects are warranted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".